A system comprises an encoder configured to compress attribute information and/or spatial information for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. In some embodiments, a bit stream structure may be used to communicate compressed point cloud data. The bit stream structure may include point cloud compression network abstraction layer (PCCNAL) units that enable use of groups of frames (GOFs), frame, and sub-frame signaling of patch information. Such a bit stream structure may permit low delay streaming and random access reconstruction of point clouds amongst other applications.

A system comprises an encoder configured to compress attribute information and/or spatial information for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. In some embodiments, a bit stream structure may be used to communicate compressed point cloud data. The bit stream structure may include point cloud compression network abstraction layer (PCCNAL) units that enable use of groups of frames (GOFs), frame, and sub-frame signaling of patch information. Such a bit stream structure may permit low delay streaming and random access reconstruction of point clouds amongst other applications.

An image decoding apparatus obtain pieces of coded data that is included in a bitstream and generated by coding tiles. Tile boundary independence information is further obtained from the bitstream, with the tile boundary independence information indicating whether each of boundaries between the tiles is one of a first boundary or a second boundary. The pieces of coded data are decoded to generate image data of the tiles. Image data of a first tile is generated by decoding a first code string included in first coded data with reference to decoding information of a decoded tile when the tile boundary independence information indicates the first boundary, and by decoding the first code string without referring to the decoding information of the decoded tile when the tile boundary independence information indicates the second boundary.

An image decoding apparatus obtain pieces of coded data that is included in a bitstream and generated by coding tiles. Tile boundary independence information is further obtained from the bitstream, with the tile boundary independence information indicating whether each of boundaries between the tiles is one of a first boundary or a second boundary. The pieces of coded data are decoded to generate image data of the tiles. Image data of a first tile is generated by decoding a first code string included in first coded data with reference to decoding information of a decoded tile when the tile boundary independence information indicates the first boundary, and by decoding the first code string without referring to the decoding information of the decoded tile when the tile boundary independence information indicates the second boundary.

A system comprises an encoder configured to compress attribute and/or spatial information for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. In some embodiments, an encoder generates time-consistent patches for multiple version of the point cloud at multiple moments in time and uses the time-consistent patches to generate image based representations of the point cloud at the multiple moments in time.

Disclosed is an image data encoding/decoding method and apparatus. A method for decoding a 360-degree image comprises the steps of: receiving a bitstream obtained by encoding a 360-degree image; generating a prediction image by making reference to syntax information obtained from the received bitstream; combining the generated prediction image with a residual image obtained by dequantizing and inverse-transforming the bitstream, so as to obtain a decoded image; and reconstructing the decoded image into a 360-degree image according to a projection format.

A moving body control system controls a moving body being a target of remote support by a remote operator. The moving body control system acquires an image captured by a camera installed on the moving body, and spatially splits the image into a plurality of split images. The moving body control system sets importance of each of the plurality of split images such that the importance of a split image with a higher need for gaze by the remote operator is higher than the importance of a split image with a lower need for the gaze by the remote operator. The moving body control system encodes and transmits each split image to a remote support device on the remote operator side such that an image quality of the split image of the higher importance is higher than an image quality of the split image of the lower importance.

An encoding device evaluates a plurality of processing and/or post-processing algorithms and/or methods to be applied to a video stream, and signals a selected method, algorithm, class or category of methods/algorithms either in an encoded bitstream or as side information related to the encoded bitstream. A decoding device or post-processor utilizes the signaled algorithm or selects an algorithm/method based on the signaled method or algorithm. The selection is based, for example, on availability of the algorithm/method at the decoder/post-processor and/or cost of implementation. The video stream may comprise, for example, downsampled multiplexed stereoscopic images and the selected algorithm may include any of upconversion and/or error correction techniques that contribute to a restoration of the downsampled images.

An encoding device evaluates a plurality of processing and/or post-processing algorithms and/or methods to be applied to a video stream, and signals a selected method, algorithm, class or category of methods/algorithms either in an encoded bitstream or as side information related to the encoded bitstream. A decoding device or post-processor utilizes the signaled algorithm or selects an algorithm/method based on the signaled method or algorithm. The selection is based, for example, on availability of the algorithm/method at the decoder/post-processor and/or cost of implementation. The video stream may comprise, for example, downsampled multiplexed stereoscopic images and the selected algorithm may include any of upconversion and/or error correction techniques that contribute to a restoration of the downsampled images.

Various embodiments of the present disclosure provide a segment-wise prediction machine learning framework. In one example, an embodiment provides for generating, using a segment-wise prediction machine learning framework, and based at least in part on a document segment for an input segment and a respective predictive code for the input segment, a segment-wise prediction score for the input segment. The segment-wise prediction machine learning framework may comprise a text embedding machine learning model and may be configured to generate a segment-wise prediction score for the input segment based at least in part on a document embedding for the input segment and a code embedding for the respective predictive code for the input segment. Additionally, the text embedding machine learning model may be trained as part of a code prediction machine learning model that is configured to generate, for a particular input document data object, a selected code subset.